[PDF][PDF] WildfireDB: A Spatio-Temporal Dataset Combining Wildfire Occurrence with Relevant Covariates
Modeling fire spread is critical in fire risk management. Creating data-driven models to
forecast spread remains challenging due to the lack of comprehensive data sources that …
forecast spread remains challenging due to the lack of comprehensive data sources that …
[PDF][PDF] Wildfiredb: An open-source dataset connecting wildfire spread with relevant determinants
S Singla, A Mukhopadhyay… - … on Datasets and …, 2021 - ayanmukhopadhyay.github.io
Modeling fire spread is critical in fire risk management. Creating data-driven models to
forecast spread remains challenging due to the lack of comprehensive data sources that …
forecast spread remains challenging due to the lack of comprehensive data sources that …
Wildfiredb: An open-source dataset connecting wildfire occurrence with relevant determinants
Modeling fire spread is critical in fire risk management. Creating data-driven models to
forecast spread remains challenging due to the lack of comprehensive data sources that …
forecast spread remains challenging due to the lack of comprehensive data sources that …
Next day wildfire spread: A machine learning dataset to predict wildfire spreading from remote-sensing data
Predicting wildfire spread is critical for land management and disaster preparedness. To this
end, we present “Next Day Wildfire Spread,” a curated, large-scale, multivariate dataset of …
end, we present “Next Day Wildfire Spread,” a curated, large-scale, multivariate dataset of …
[PDF][PDF] FireCast: Leveraging Deep Learning to Predict Wildfire Spread.
D Radke, A Hessler, D Ellsworth - IJCAI, 2019 - ijcai.org
Destructive wildfires result in billions of dollars in damage each year and are expected to
increase in frequency, duration, and severity due to climate change. The current state-of-the …
increase in frequency, duration, and severity due to climate change. The current state-of-the …
WildfireSpreadTS: A dataset of multi-modal time series for wildfire spread prediction
We present a multi-temporal, multi-modal remote-sensing dataset for predicting how active
wildfires will spread at a resolution of 24 hours. The dataset consists of 13607 images …
wildfires will spread at a resolution of 24 hours. The dataset consists of 13607 images …
Learning Wildfire Model from Incomplete State Observations
A Chavalithumrong, HJ Yoon, P Voulgaris - arXiv preprint arXiv …, 2021 - arxiv.org
As wildfires are expected to become more frequent and severe, improved prediction models
are vital to mitigating risk and allocating resources. With remote sensing data, valuable …
are vital to mitigating risk and allocating resources. With remote sensing data, valuable …
Deep learning models for predicting wildfires from historical remote-sensing data
Identifying regions that have high likelihood for wildfires is a key component of land and
forestry management and disaster preparedness. We create a data set by aggregating …
forestry management and disaster preparedness. We create a data set by aggregating …
[BOOK][B] Some results on a set of data driven stochastic wildfire models
ME Green - 2020 - search.proquest.com
Across the globe, the frequency and size of wildfire events are increasing. Research focused
on minimizing wildfire is critically needed to mitigate impending humanitarian and …
on minimizing wildfire is critically needed to mitigate impending humanitarian and …
Forest fire prediction using heterogeneous data sources and machine learning methods
P Kaur - 2023 - uwspace.uwaterloo.ca
Forest fires pose a significant and urgent threat to ecosystems and human lives,
necessitating accurate prediction for effective mitigation strategies. Predicting forest fires has …
necessitating accurate prediction for effective mitigation strategies. Predicting forest fires has …